Overview

Dataset statistics

Number of variables12
Number of observations663
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory212.7 KiB
Average record size in memory328.5 B

Variable types

Numeric6
Categorical6

Alerts

ClusterID is highly overall correlated with Customer_Category and 4 other fieldsHigh correlation
Customer_Category is highly overall correlated with ClusterID and 6 other fieldsHigh correlation
Frequency is highly overall correlated with RFM_SumHigh correlation
Frequency_Score is highly overall correlated with Customer_Category and 1 other fieldsHigh correlation
ID is highly overall correlated with K-Means Segments and 4 other fieldsHigh correlation
K-Means Segments is highly overall correlated with ClusterID and 5 other fieldsHigh correlation
Monetary is highly overall correlated with RFM_SumHigh correlation
Monetary_Score is highly overall correlated with Customer_Category and 2 other fieldsHigh correlation
RFM Score is highly overall correlated with ClusterID and 5 other fieldsHigh correlation
RFM_Sum is highly overall correlated with Frequency and 4 other fieldsHigh correlation
Recency is highly overall correlated with ClusterID and 4 other fieldsHigh correlation
Recency_Score is highly overall correlated with ClusterID and 5 other fieldsHigh correlation
Recency_Score is uniformly distributedUniform
ID has unique valuesUnique

Reproduction

Analysis started2024-03-18 04:07:21.981110
Analysis finished2024-03-18 04:09:28.951682
Duration2 minutes and 6.97 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct663
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1230800.8
Minimum1230001
Maximum1231797
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-03-18T11:09:29.620282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1230001
5-th percentile1230049.1
Q11230307.5
median1230745
Q31231278
95-th percentile1231690.9
Maximum1231797
Range1796
Interquartile range (IQR)970.5

Descriptive statistics

Standard deviation545.5876
Coefficient of variation (CV)0.00044327855
Kurtosis-1.2708628
Mean1230800.8
Median Absolute Deviation (MAD)485
Skewness0.21058599
Sum8.1602094 × 108
Variance297665.82
MonotonicityStrictly increasing
2024-03-18T11:09:30.185366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1230001 1
 
0.2%
1231119 1
 
0.2%
1231123 1
 
0.2%
1231126 1
 
0.2%
1231129 1
 
0.2%
1231134 1
 
0.2%
1231135 1
 
0.2%
1231137 1
 
0.2%
1231139 1
 
0.2%
1231146 1
 
0.2%
Other values (653) 653
98.5%
ValueCountFrequency (%)
1230001 1
0.2%
1230002 1
0.2%
1230003 1
0.2%
1230004 1
0.2%
1230005 1
0.2%
1230006 1
0.2%
1230007 1
0.2%
1230008 1
0.2%
1230009 1
0.2%
1230010 1
0.2%
ValueCountFrequency (%)
1231797 1
0.2%
1231793 1
0.2%
1231789 1
0.2%
1231788 1
0.2%
1231787 1
0.2%
1231782 1
0.2%
1231781 1
0.2%
1231771 1
0.2%
1231763 1
0.2%
1231760 1
0.2%

Monetary
Real number (ℝ)

HIGH CORRELATION 

Distinct445
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51316415
Minimum60000
Maximum4.31943 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-03-18T11:09:31.253605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum60000
5-th percentile60000
Q1300000
median2090000
Q338046000
95-th percentile4.303797 × 108
Maximum4.31943 × 108
Range4.31883 × 108
Interquartile range (IQR)37746000

Descriptive statistics

Standard deviation1.1112209 × 108
Coefficient of variation (CV)2.1654297
Kurtosis5.8548884
Mean51316415
Median Absolute Deviation (MAD)2025000
Skewness2.6380496
Sum3.4022783 × 1010
Variance1.2348119 × 1016
MonotonicityNot monotonic
2024-03-18T11:09:31.653862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 39
 
5.9%
431943000 34
 
5.1%
65000 17
 
2.6%
85000 13
 
2.0%
75000 9
 
1.4%
140000 7
 
1.1%
220000 6
 
0.9%
95000 6
 
0.9%
240000 6
 
0.9%
320000 5
 
0.8%
Other values (435) 521
78.6%
ValueCountFrequency (%)
60000 39
5.9%
65000 17
2.6%
70000 2
 
0.3%
75000 9
 
1.4%
80000 1
 
0.2%
80320 1
 
0.2%
85000 13
 
2.0%
90000 4
 
0.6%
95000 6
 
0.9%
100000 3
 
0.5%
ValueCountFrequency (%)
431943000 34
5.1%
416310000 1
 
0.2%
411740000 1
 
0.2%
407295000 1
 
0.2%
405600000 1
 
0.2%
393040000 1
 
0.2%
391620000 1
 
0.2%
384686000 1
 
0.2%
378175000 1
 
0.2%
353970000 1
 
0.2%

Frequency
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7119155
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-03-18T11:09:32.014956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile9
Maximum53
Range52
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.9021212
Coefficient of variation (CV)1.4388801
Kurtosis58.104459
Mean2.7119155
Median Absolute Deviation (MAD)0
Skewness6.196475
Sum1798
Variance15.22655
MonotonicityNot monotonic
2024-03-18T11:09:32.335198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 337
50.8%
2 153
23.1%
3 46
 
6.9%
4 34
 
5.1%
5 24
 
3.6%
7 13
 
2.0%
6 12
 
1.8%
9 9
 
1.4%
8 9
 
1.4%
13 8
 
1.2%
Other values (10) 18
 
2.7%
ValueCountFrequency (%)
1 337
50.8%
2 153
23.1%
3 46
 
6.9%
4 34
 
5.1%
5 24
 
3.6%
6 12
 
1.8%
7 13
 
2.0%
8 9
 
1.4%
9 9
 
1.4%
10 3
 
0.5%
ValueCountFrequency (%)
53 1
 
0.2%
38 1
 
0.2%
29 1
 
0.2%
26 1
 
0.2%
24 1
 
0.2%
19 2
 
0.3%
15 3
 
0.5%
14 1
 
0.2%
13 8
1.2%
11 4
0.6%

Recency
Real number (ℝ)

HIGH CORRELATION 

Distinct298
Distinct (%)44.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean276.819
Minimum0
Maximum706
Zeros3
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-03-18T11:09:32.703481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q1110
median238
Q3457
95-th percentile629
Maximum706
Range706
Interquartile range (IQR)347

Descriptive statistics

Standard deviation198.50706
Coefficient of variation (CV)0.71710057
Kurtosis-1.0457902
Mean276.819
Median Absolute Deviation (MAD)153
Skewness0.46977125
Sum183531
Variance39405.055
MonotonicityNot monotonic
2024-03-18T11:09:33.119787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 16
 
2.4%
124 15
 
2.3%
116 15
 
2.3%
72 11
 
1.7%
101 9
 
1.4%
142 9
 
1.4%
109 9
 
1.4%
76 9
 
1.4%
4 7
 
1.1%
35 6
 
0.9%
Other values (288) 557
84.0%
ValueCountFrequency (%)
0 3
0.5%
1 4
0.6%
4 7
1.1%
6 4
0.6%
8 1
 
0.2%
9 2
 
0.3%
30 3
0.5%
31 5
0.8%
32 3
0.5%
33 5
0.8%
ValueCountFrequency (%)
706 1
 
0.2%
705 4
0.6%
702 1
 
0.2%
701 1
 
0.2%
698 3
0.5%
697 1
 
0.2%
685 1
 
0.2%
683 1
 
0.2%
681 1
 
0.2%
675 1
 
0.2%

Recency_Score
Categorical

HIGH CORRELATION  UNIFORM 

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size37.7 KiB
3
133 
4
133 
1
133 
2
132 
5
132 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row4
4th row4
5th row1

Common Values

ValueCountFrequency (%)
3 133
20.1%
4 133
20.1%
1 133
20.1%
2 132
19.9%
5 132
19.9%

Length

2024-03-18T11:09:33.520166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:09:33.920835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 133
20.1%
4 133
20.1%
1 133
20.1%
2 132
19.9%
5 132
19.9%

Most occurring characters

ValueCountFrequency (%)
3 133
20.1%
4 133
20.1%
1 133
20.1%
2 132
19.9%
5 132
19.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 133
20.1%
4 133
20.1%
1 133
20.1%
2 132
19.9%
5 132
19.9%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 133
20.1%
4 133
20.1%
1 133
20.1%
2 132
19.9%
5 132
19.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 133
20.1%
4 133
20.1%
1 133
20.1%
2 132
19.9%
5 132
19.9%

Frequency_Score
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size37.7 KiB
1
337 
3
153 
5
127 
4
46 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row3
4th row3
5th row5

Common Values

ValueCountFrequency (%)
1 337
50.8%
3 153
23.1%
5 127
 
19.2%
4 46
 
6.9%

Length

2024-03-18T11:09:34.270341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:09:34.617807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 337
50.8%
3 153
23.1%
5 127
 
19.2%
4 46
 
6.9%

Most occurring characters

ValueCountFrequency (%)
1 337
50.8%
3 153
23.1%
5 127
 
19.2%
4 46
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 337
50.8%
3 153
23.1%
5 127
 
19.2%
4 46
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 337
50.8%
3 153
23.1%
5 127
 
19.2%
4 46
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 337
50.8%
3 153
23.1%
5 127
 
19.2%
4 46
 
6.9%

Monetary_Score
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size37.7 KiB
1
135 
3
133 
5
133 
4
132 
2
130 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row5
4th row4
5th row5

Common Values

ValueCountFrequency (%)
1 135
20.4%
3 133
20.1%
5 133
20.1%
4 132
19.9%
2 130
19.6%

Length

2024-03-18T11:09:34.954207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:09:35.346525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 135
20.4%
3 133
20.1%
5 133
20.1%
4 132
19.9%
2 130
19.6%

Most occurring characters

ValueCountFrequency (%)
1 135
20.4%
3 133
20.1%
5 133
20.1%
4 132
19.9%
2 130
19.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 135
20.4%
3 133
20.1%
5 133
20.1%
4 132
19.9%
2 130
19.6%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 135
20.4%
3 133
20.1%
5 133
20.1%
4 132
19.9%
2 130
19.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 135
20.4%
3 133
20.1%
5 133
20.1%
4 132
19.9%
2 130
19.6%

RFM Score
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean327.20513
Minimum111
Maximum553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-03-18T11:09:35.746932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum111
5-th percentile114
Q1212
median332
Q3435
95-th percentile515
Maximum553
Range442
Interquartile range (IQR)223

Descriptive statistics

Standard deviation136.88237
Coefficient of variation (CV)0.41833809
Kurtosis-1.3123914
Mean327.20513
Median Absolute Deviation (MAD)118
Skewness-0.017794061
Sum216937
Variance18736.783
MonotonicityNot monotonic
2024-03-18T11:09:36.155217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
511 34
 
5.1%
155 27
 
4.1%
411 26
 
3.9%
211 22
 
3.3%
513 22
 
3.3%
512 21
 
3.2%
311 20
 
3.0%
212 18
 
2.7%
515 15
 
2.3%
111 15
 
2.3%
Other values (78) 443
66.8%
ValueCountFrequency (%)
111 15
2.3%
112 9
1.4%
113 5
 
0.8%
114 7
1.1%
115 5
 
0.8%
131 3
 
0.5%
132 7
1.1%
133 7
1.1%
134 5
 
0.8%
135 3
 
0.5%
ValueCountFrequency (%)
553 1
 
0.2%
552 1
 
0.2%
545 1
 
0.2%
544 1
 
0.2%
543 1
 
0.2%
535 2
 
0.3%
534 6
0.9%
533 4
0.6%
532 8
1.2%
531 4
0.6%

RFM_Sum
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4313725
Minimum3
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.3 KiB
2024-03-18T11:09:36.491430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q17
median9
Q310
95-th percentile12
Maximum14
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4882283
Coefficient of variation (CV)0.29511545
Kurtosis-0.70530335
Mean8.4313725
Median Absolute Deviation (MAD)2
Skewness-0.1742135
Sum5590
Variance6.1912801
MonotonicityNot monotonic
2024-03-18T11:09:36.811640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 96
14.5%
10 96
14.5%
11 91
13.7%
7 88
13.3%
8 71
10.7%
6 66
10.0%
5 46
6.9%
12 37
 
5.6%
4 31
 
4.7%
13 22
 
3.3%
Other values (2) 19
 
2.9%
ValueCountFrequency (%)
3 15
 
2.3%
4 31
 
4.7%
5 46
6.9%
6 66
10.0%
7 88
13.3%
8 71
10.7%
9 96
14.5%
10 96
14.5%
11 91
13.7%
12 37
 
5.6%
ValueCountFrequency (%)
14 4
 
0.6%
13 22
 
3.3%
12 37
 
5.6%
11 91
13.7%
10 96
14.5%
9 96
14.5%
8 71
10.7%
7 88
13.3%
6 66
10.0%
5 46
6.9%

Customer_Category
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size45.5 KiB
New Customers
114 
Promissing
102 
At Risk
89 
Cannot Lose Them
80 
Potential Loyalist
75 
Other values (6)
203 

Length

Max length21
Median length14
Mean length13.010558
Min length5

Characters and Unicode

Total characters8626
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAt Risk
2nd rowPotential Loyalist
3rd rowLoyal
4th rowNeed Attention
5th rowCannot Lose Them

Common Values

ValueCountFrequency (%)
New Customers 114
17.2%
Promissing 102
15.4%
At Risk 89
13.4%
Cannot Lose Them 80
12.1%
Potential Loyalist 75
11.3%
Hibernating Customers 62
9.4%
Loyal 35
 
5.3%
Need Attention 35
 
5.3%
About To Sleep 28
 
4.2%
Lost Customers 28
 
4.2%

Length

2024-03-18T11:09:37.203931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
customers 204
15.9%
new 114
 
8.9%
promissing 102
 
8.0%
at 89
 
6.9%
risk 89
 
6.9%
cannot 80
 
6.2%
lose 80
 
6.2%
them 80
 
6.2%
loyalist 75
 
5.9%
potential 75
 
5.9%
Other values (9) 294
22.9%

Most occurring characters

ValueCountFrequency (%)
s 899
 
10.4%
t 821
 
9.5%
o 785
 
9.1%
e 776
 
9.0%
619
 
7.2%
i 617
 
7.2%
n 546
 
6.3%
m 401
 
4.6%
r 368
 
4.3%
a 342
 
4.0%
Other values (19) 2452
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6725
78.0%
Uppercase Letter 1282
 
14.9%
Space Separator 619
 
7.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 899
13.4%
t 821
12.2%
o 785
11.7%
e 776
11.5%
i 617
9.2%
n 546
8.1%
m 401
6.0%
r 368
 
5.5%
a 342
 
5.1%
u 232
 
3.4%
Other values (9) 938
13.9%
Uppercase Letter
ValueCountFrequency (%)
C 299
23.3%
L 218
17.0%
P 177
13.8%
A 152
11.9%
N 149
11.6%
T 108
 
8.4%
R 89
 
6.9%
H 62
 
4.8%
S 28
 
2.2%
Space Separator
ValueCountFrequency (%)
619
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8007
92.8%
Common 619
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 899
11.2%
t 821
 
10.3%
o 785
 
9.8%
e 776
 
9.7%
i 617
 
7.7%
n 546
 
6.8%
m 401
 
5.0%
r 368
 
4.6%
a 342
 
4.3%
C 299
 
3.7%
Other values (18) 2153
26.9%
Common
ValueCountFrequency (%)
619
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8626
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 899
 
10.4%
t 821
 
9.5%
o 785
 
9.1%
e 776
 
9.0%
619
 
7.2%
i 617
 
7.2%
n 546
 
6.3%
m 401
 
4.6%
r 368
 
4.3%
a 342
 
4.0%
Other values (19) 2452
28.4%

ClusterID
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size37.7 KiB
1
374 
2
232 
0
57 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters663
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row2
5th row0

Common Values

ValueCountFrequency (%)
1 374
56.4%
2 232
35.0%
0 57
 
8.6%

Length

2024-03-18T11:09:37.636191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:09:38.004451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 374
56.4%
2 232
35.0%
0 57
 
8.6%

Most occurring characters

ValueCountFrequency (%)
1 374
56.4%
2 232
35.0%
0 57
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 374
56.4%
2 232
35.0%
0 57
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
Common 663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 374
56.4%
2 232
35.0%
0 57
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 374
56.4%
2 232
35.0%
0 57
 
8.6%

K-Means Segments
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size48.2 KiB
Regular Customers
374 
Inactive Customers
232 
Premium Shoppers
57 

Length

Max length18
Median length17
Mean length17.263952
Min length16

Characters and Unicode

Total characters11446
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRegular Customers
2nd rowRegular Customers
3rd rowPremium Shoppers
4th rowInactive Customers
5th rowPremium Shoppers

Common Values

ValueCountFrequency (%)
Regular Customers 374
56.4%
Inactive Customers 232
35.0%
Premium Shoppers 57
 
8.6%

Length

2024-03-18T11:09:38.364503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:09:38.749006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
customers 606
45.7%
regular 374
28.2%
inactive 232
 
17.5%
premium 57
 
4.3%
shoppers 57
 
4.3%

Most occurring characters

ValueCountFrequency (%)
e 1326
11.6%
s 1269
11.1%
r 1094
9.6%
u 1037
 
9.1%
t 838
 
7.3%
m 720
 
6.3%
o 663
 
5.8%
663
 
5.8%
a 606
 
5.3%
C 606
 
5.3%
Other values (12) 2624
22.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9457
82.6%
Uppercase Letter 1326
 
11.6%
Space Separator 663
 
5.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1326
14.0%
s 1269
13.4%
r 1094
11.6%
u 1037
11.0%
t 838
8.9%
m 720
7.6%
o 663
7.0%
a 606
6.4%
l 374
 
4.0%
g 374
 
4.0%
Other values (6) 1156
12.2%
Uppercase Letter
ValueCountFrequency (%)
C 606
45.7%
R 374
28.2%
I 232
 
17.5%
P 57
 
4.3%
S 57
 
4.3%
Space Separator
ValueCountFrequency (%)
663
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10783
94.2%
Common 663
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1326
12.3%
s 1269
11.8%
r 1094
10.1%
u 1037
9.6%
t 838
 
7.8%
m 720
 
6.7%
o 663
 
6.1%
a 606
 
5.6%
C 606
 
5.6%
R 374
 
3.5%
Other values (11) 2250
20.9%
Common
ValueCountFrequency (%)
663
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11446
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1326
11.6%
s 1269
11.1%
r 1094
9.6%
u 1037
 
9.1%
t 838
 
7.3%
m 720
 
6.3%
o 663
 
5.8%
663
 
5.8%
a 606
 
5.3%
C 606
 
5.3%
Other values (12) 2624
22.9%

Interactions

2024-03-18T11:09:14.487119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:07:31.820611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:23.577476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:35.370651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:47.124802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:59.972174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:09:23.772807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:07:53.493039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:32.259040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:44.315895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:57.308205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:09:09.468645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:09:24.082020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:07:58.985322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:32.571463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:44.724306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:57.654164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:09:10.355908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:09:24.400029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:05.301123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:32.924617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:45.092898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:57.999666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:09:11.183450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:09:24.805352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:10.794038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:33.299811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:45.471236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:58.354618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:09:12.050704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:09:27.389537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:17.270530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:35.009496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:46.789345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:08:59.581242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-18T11:09:13.672919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-03-18T11:09:39.045770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ClusterIDCustomer_CategoryFrequencyFrequency_ScoreIDK-Means SegmentsMonetaryMonetary_ScoreRFM ScoreRFM_SumRecencyRecency_Score
ClusterID1.0000.549-0.3290.238-0.2581.000-0.3750.4390.676-0.0300.7350.650
Customer_Category0.5491.000-0.4000.575-0.1980.549-0.1060.5420.6720.0850.7060.604
Frequency-0.329-0.4001.0000.350-0.4420.2240.4090.098-0.1590.686-0.3570.162
Frequency_Score0.2380.5750.3501.000-0.4350.2380.4020.240-0.1500.689-0.3490.219
ID-0.258-0.198-0.442-0.4351.0001.000-0.1531.000-0.515-0.585-0.3991.000
K-Means Segments1.0000.5490.2240.2381.0001.0000.0650.439-0.762-0.232-0.7720.650
Monetary-0.375-0.1060.4090.402-0.1530.0651.0000.470-0.0130.735-0.1700.019
Monetary_Score0.4390.5420.0980.2401.0000.4390.4701.000-0.0180.731-0.1720.046
RFM Score0.6760.672-0.159-0.150-0.515-0.762-0.013-0.0181.0000.4150.9530.935
RFM_Sum-0.0300.0850.6860.689-0.585-0.2320.7350.7310.4151.0000.1950.261
Recency0.7350.706-0.357-0.349-0.399-0.772-0.170-0.1720.9530.1951.0000.867
Recency_Score0.6500.6040.1620.2191.0000.6500.0190.0460.9350.2610.8671.000

Missing values

2024-03-18T11:09:27.883872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T11:09:28.561895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDMonetaryFrequencyRecencyRecency_ScoreFrequency_ScoreMonetary_ScoreRFM ScoreRFM_SumCustomer_CategoryClusterIDK-Means Segments
01230001905000.00031372432439At Risk1Regular Customers
112300024833000.000425835335311Potential Loyalist1Regular Customers
21230003431943000.000234643543512Loyal0Premium Shoppers
3123000428800000.000234343443411Need Attention2Inactive Customers
41230005220728000.000157015515511Cannot Lose Them0Premium Shoppers
51230006174170000.000910925525512At Risk1Regular Customers
61230007189442000.00053915515511Cannot Lose Them0Premium Shoppers
712300086783000.0002416235335311Potential Loyalist1Regular Customers
81230009220000.00022843323328Hibernating Customers1Regular Customers
912300102790000.000516235335311Potential Loyalist1Regular Customers
IDMonetaryFrequencyRecencyRecency_ScoreFrequency_ScoreMonetary_ScoreRFM ScoreRFM_SumCustomer_CategoryClusterIDK-Means Segments
653123176075000.000101111113Lost Customers1Regular Customers
65412317632520000.0001581131135Cannot Lose Them1Regular Customers
65512317711065000.0002481331337At Risk1Regular Customers
6561231781810000.0001501121124Lost Customers1Regular Customers
6571231782680000.0001501121124Lost Customers1Regular Customers
658123178765000.0001441111113Lost Customers1Regular Customers
659123178860000.0001441111113Lost Customers1Regular Customers
6601231789500000.0001441121124Lost Customers1Regular Customers
6611231793140000.0001411111113Lost Customers1Regular Customers
66212317971000000.0001411131135Cannot Lose Them1Regular Customers